Anisotropic noise injection for input variables relevance determination

2000 ◽  
Vol 11 (6) ◽  
pp. 1201-1212 ◽  
Author(s):  
Y. Grandvalet
Keyword(s):  
Noise Injection ◽  
Input Variables

Path Tracking Controller Design of Automatic Guided Vehicle Based on Four-Wheeled Omnidirectional Motion Model

2020 ◽  
Vol 17 (2) ◽  
pp. 7996-8010
Author(s):  
X. Wu ◽  
Y. Yang
Keyword(s):  
Control System ◽  
Fuzzy Controller ◽  
Controller Design ◽  
Threshold Value ◽  
Position Angle ◽  
Path Tracking ◽  
Global Coordinate System ◽  
Automatic Guided Vehicle ◽  
Input Variables ◽  
Industrial Pc

This paper presents a new design of omnidirectional automatic guided vehicle based on a hub motor, and proposes a joint controller for path tracking. The proposed controller includes two parts: a fuzzy controller and a multi-step predictive optimal controller. Firstly, based on various steering conditions, the kinematics model of the whole vehicle and the pose (position, angle) model in the global coordinate system are introduced. Secondly, based on the modeling, the joint controller is designed. Lateral deviation and course deviation are used as the input variables of the control system, and the threshold value is switched according to the value of the input variable to realise the correction of the large range of posture deviation. Finally, the joint controller is implemented by using the industrial PC and the self-developed control system based on the Freescale minimum system. Path tracking experiments were made under the straight and circular paths to test the ability of the joint controller for reducing the pose deviation. The experimental results show that the designed guided vehicle has excellent ability to path tracking, which meets the design goals.

Analysis of Constrained Optimal Control with Related Constraints of Input Variables

2014 ◽  
Vol 39 (5) ◽  
pp. 679-689 ◽  
Author(s):  
Xiong-Lin LUO ◽  
Xiao-Long ZHOU ◽  
Shu-Bin WANG
Keyword(s):  
Optimal Control ◽  
Constrained Optimal Control ◽  
Input Variables

A Primer on the Assessment of Economic Impacts of Leisure Events

2020 ◽  
Vol 24 (1) ◽  
pp. 47-56
Author(s):  
Ove Oklevik ◽  
Grzegorz Kwiatkowski ◽  
Mona Kristin Nytun ◽  
Helene Maristuen
Keyword(s):  
Impact Assessment ◽  
Economic Impact ◽  
Study Design ◽  
Impact Analysis ◽  
Sensitivity Analyses ◽  
Primary Data ◽  
Music Festival ◽  
Economic Impact Analysis ◽  
Economic Impact Assessment ◽  
Input Variables

The quality of any economic impact assessment largely depends on the adequacy of the input variables and chosen assumptions. This article presents a direct economic impact assessment of a music festival hosted in Norway and sensitivity analyses of two study design assumptions: estimated number of attendees and chosen definition (size) of the affected area. Empirically, the article draws on a state-of-the-art framework of an economic impact analysis and uses primary data from 471 event attendees. The results show that, first, an economic impact analysis is a complex task that requires high precision in assessing different monetary flows entering and leaving the host region, and second, the study design assumptions exert a tremendous influence on the final estimation. Accordingly, the study offers a fertile agenda for local destination marketing organizers and event managers on how to conduct reliable economic impact assessments and explains which elements of such analyses are particularly important for final estimations.

Stormwater inflow prediction using radar rainfall data compressed by principal component analysis

2006 ◽  
Vol 1 (1) ◽  
Author(s):  
K. Katayama ◽  
K. Kimijima ◽  
O. Yamanaka ◽  
A. Nagaiwa ◽  
Y. Ono
Keyword(s):  
Principal Component Analysis ◽  
Prediction Model ◽  
Principal Components ◽  
Prediction Method ◽  
Principal Component ◽  
Component Analysis ◽  
Rainfall Data ◽  
Radar Rainfall ◽  
Input Variables ◽  
Inflow Prediction

This paper proposes a method of stormwater inflow prediction using radar rainfall data as the input of the prediction model constructed by system identification. The aim of the proposal is to construct a compact system by reducing the dimension of the input data. In this paper, Principal Component Analysis (PCA), which is widely used as a statistical method for data analysis and compression, is applied to pre-processing radar rainfall data. Then we evaluate the proposed method using the radar rainfall data and the inflow data acquired in a certain combined sewer system. This study reveals that a few principal components of radar rainfall data can be appropriate as the input variables to storm water inflow prediction model. Consequently, we have established a procedure for the stormwater prediction method using a few principal components of radar rainfall data.

Using Decomposition Kinetics to Model the Removal of Mine Water Pollutants in Constructed Wetlands

1994 ◽  
Vol 29 (4) ◽  
pp. 219-226 ◽  
Author(s):  
William J. Tarutis ◽  
Richard F. Unz
Keyword(s):  
Organic Matter ◽  
Constructed Wetlands ◽  
Mine Drainage ◽  
Term Treatment ◽  
Decomposition Kinetics ◽  
Dry Density ◽  
Organic Matter Concentration ◽  
Long Term Treatment ◽  
Input Variables ◽  
Reaction Stoichiometry

Although numerous mathematical models have been used to describe decomposition, few, if any, have been used to model the removal of pollutants in constructed wetlands. A steady-state model based on decomposition kinetics and reaction stoichiometry has been developed which simulates the removal of ferrous iron entering wetlands constructed for mine drainage treatment. Input variables for the model include organic matter concentration, reaction rate coefficient, porosity and dry density, and hydraulic detention time. Application of the model assumes complete anaerobic conditions within the entire substrate profile, constant temperature, no additional organic matter input, and subsurface flow only. For these ideal conditions, model simulations indicate that wetlands constructed with readily decomposable substrates rich in organic carbon are initially capable of removing far greater amounts of iron than wetlands built with less biodegradable substrates. However, after three to five years of operation this difference becomes negligible. For acceptable long-term treatment performance, therefore, periodic additions of decomposable organic matter will be required.

Demulsification of a Water-in-crude Oil Emulsion with a Corn Oil BasedDemulsifier using the Response Surface Methodology: Modelling and Optimization

2021 ◽  
Vol 14 ◽  
Author(s):  
Abed Saad ◽  
Nour Abdurahman ◽  
Rosli Mohd Yunus
Keyword(s):  
Response Surface Methodology ◽  
Crude Oil ◽  
Water Content ◽  
Response Surface ◽  
Separation Efficiency ◽  
Corn Oil ◽  
Oil Emulsion ◽  
Optimal Values ◽  
Input Variables ◽  
Oil Emulsions

: In this study, the Sany-glass test was used to evaluate the performance of a new surfactant prepared from corn oil as a demulsifier for crude oil emulsions. Central composite design (CCD), based on the response surface methodology (RSM), was used to investigate the effect of four variables, including demulsifier dosage, water content, temperature, and pH, on the efficiency of water removal from the emulsion. As well, analysis of variance was applied to examine the precision of the CCD mathematical model. The results indicate that demulsifier dose and emulsion pH are two significant parameters determining demulsification. The maximum separation efficiency of 96% was attained at an alkaline pH and with 3500 ppm demulsifier. According to the RSM analysis, the optimal values for the input variables are 40% water content, 3500 ppm demulsifier, 60 °C, and pH 8.

scholarly journals From Exploratory Tunnel to Base Tunnel: Hard Rock TBM Performance Prediction by Means of a Stochastic Approach

2020 ◽  
Vol 53 (12) ◽  
pp. 5473-5487 ◽  
Author(s):  
Andrea Rispoli ◽  
Anna Maria Ferrero ◽  
Marilena Cardu
Keyword(s):  
Performance Prediction ◽  
Early Stage ◽  
Tunnel Boring Machine ◽  
Stochastic Approach ◽  
Critical Issue ◽  
Utilization Factor ◽  
Tbm Performance ◽  
Factors Affecting ◽  
Tunnel Section ◽  
Input Variables

AbstractTunnel boring machine (TBM) performance prediction is often a critical issue in the early stage of a tunnelling project, mainly due to the unpredictable nature of some important factors affecting the machine performance. In this regard, deterministic approaches are normally employed, providing results in terms of average values expected for the TBM performance. Stochastic approaches would offer improvement over deterministic methods, taking into account the parameter variability; however, their use is limited, since the level of information required is often not available. In this study, the data provided by the excavation of the Maddalena exploratory tunnel were used to predict the net and overall TBM performance for a 2.96 km section of the Mont Cenis base tunnel by using a stochastic approach. The preliminary design of the TBM cutterhead was carried out. A prediction model based on field penetration index, machine operating level and utilization factor was adopted. The variability of the parameters involved was analysed. A procedure to take into account the correlation between the input variables was described. The probability of occurrence of the outcomes was evaluated, and the total excavation time expected for the tunnel section analysed was calculated.

scholarly journals Reproductive Performance and Partial Budget Analysis of a Prostaglandin or a Modified Ovsyn3ch Protocol in Autumn Calving Dairy Herds

Animals ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 1031
Author(s):  
George Lindley ◽  
Jim Willshire ◽  
Steven Van Winden
Keyword(s):  
Relative Effectiveness ◽  
Dairy Herds ◽  
Net Benefit ◽  
Start Date ◽  
Budget Analysis ◽  
Partial Budget ◽  
Holstein Friesian ◽  
Input Variables ◽  
Input Uncertainties ◽  
Relative Profitability

In autumn calving dairy herds, treatment of cattle not observed in estrus prior to the breeding season is common. Routinely, a single prostaglandin or a modified Ovsynch (MOFT) protocol are used—without evidence of their relative effectiveness. This study compares the effects on conception, associated timing, and profitability of administering cows with prostaglandin or MOFT treatment. A hundred and ninety-two Holstein-Friesian cows from three herds without an observed estrus within 28-days before mating start date were randomly treated with d-cloprostenol (PGOD) or an 8-day MOFT protocol. The association of treatment and calving-breeding start-date interval (CBSI) on the risk of conception were investigated. Partial budget, sensitivity analysis, and Monte Carlo simulation was used to assess economic performance, identify critical input variables, and explore the effects of input uncertainties on model output. There was a significant association between MOFT treatment and conception during 21 and 84 days after mating start date, compared to PGOD. MOFT treatment was associated with a mean net benefit of £58.21 (sd £19.42) and £27.29 (sd £17.75) per cow for herds with a fixed or variable dry-off date, respectively. The relative profitability of an MOFT protocol is dependent on its effects on barren rate and herd dry-off strategy.

scholarly journals Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Charles Gbenga Williams ◽  
Oluwapelumi O. Ojuri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydraulic Conductivity ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Coarse Grained ◽  
Soil Types ◽  
Wide Range ◽  
Artificial Neural ◽  
Input Variables

AbstractAs a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better.

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